/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.iotdb.library.util;

import org.apache.iotdb.udf.api.exception.UDFException;

import java.util.Arrays;

/**
 * This class offers function to do linear regression. Please use org.apache.commons.math3 for
 * regular usage.
 */
public class LinearRegression {
    double[] x, y, e, yhead;
    int n;
    double sumx, sumy, xbar, ybar, xxbar, yybar, xybar;
    double beta1, beta0, rss, ssr, R2, svar, svar1, svar0;

    public LinearRegression(double[] a, double[] b) throws Exception {
        x = a.clone();
        y = b.clone();
        n = x.length;
        if (x.length == 0 || y.length == 0) {
            throw new Exception("Empty input array(s).");
        }
        if (x.length != y.length) {
            throw new Exception("Different input array length.");
        }
        if (x.length == 1) { // cannot do regression
            throw new Exception("Input series should be longer than 1.");
        }
        e = new double[n];
        yhead = new double[n];
        sumx = Arrays.stream(x).sum();
        sumy = Arrays.stream(y).sum();
        xbar = sumx / n;
        ybar = sumy / n;
        // second pass: compute summary statistics
        xxbar = 0.0;
        yybar = 0.0;
        xybar = 0.0;
        for (int i = 0; i < n; i++) {
            xxbar += (x[i] - xbar) * (x[i] - xbar);
            yybar += (y[i] - ybar) * (y[i] - ybar);
            xybar += (x[i] - xbar) * (y[i] - ybar);
        }
        if (xxbar == 0d) {
            throw new UDFException("All input x are same.");
        }
        beta1 = xybar / xxbar;
        beta0 = ybar - beta1 * xbar;
        // analyze results
        int df = n - 2;
        rss = 0.0; // residual sum of squares
        ssr = 0.0; // regression sum of squares
        for (int i = 0; i < n; i++) {
            yhead[i] = beta1 * x[i] + beta0;
            e[i] = yhead[i] - y[i];
            rss += (yhead[i] - y[i]) * (yhead[i] - y[i]);
            ssr += (yhead[i] - ybar) * (yhead[i] - ybar);
        }
        R2 = ssr / yybar;
        svar = rss / df;
        svar1 = svar / xxbar;
        svar0 = svar / n + xbar * xbar * svar1;
    }

    public double getMSE() {
        return rss / n;
    }

    public double getMAbsE() { // mean abs error
        double sumAbsE = 0.0;
        for (int i = 0; i < n; i++) {
            sumAbsE += Math.abs(e[i]);
        }
        return sumAbsE / n;
    }

    public double[] getYhead() {
        return yhead;
    }
}
